Benchmarks:
The proposed algorithms are compared with state-of-the-art approaches for
\((i)\)  connected graphs: GLasso [1]}, which uses \(\ell _1\) -norm as a sparse-promoting penalty, and NGL [2]}, which use a Gaussian Laplacian-constrained model with a concave penalty regularization;
\((ii)\)  multi-component graphs: SGL [3]}, which constraints the number of graph components in an NGL-type formulation, and StGL [4]}, which generalizes the above to \(t\) -distributed data.
